Multi-hop natural language processing (NLP) refers to processing natural language texts that involve multi-hop reasoning steps, possibly across multiple sentences within a single document or across multiple documents. Most NLP tasks such as NER, relation extraction, part-of-speech (POS) tagging, etc. focus on a single sentence. However, multi-hop NLP tasks have recently received more attention from the research community. Reading comprehension (RC) has become very popular with the advances made in deep neural network research. SQuAD [1]} is a popular RC dataset that contains more than 100,000 questions based on Wikipedia articles. These questions are created by crowd workers in such a way that models need to focus on multiple sentences of an article to answer correctly. Multi-RC [2]} is another multiple-choice reading comprehension dataset, where models need to focus on multiple sentences within the same paragraph to answer the question. SquAD and Multi-RC datasets are about multi-hop reasoning across sentences within a single passage, whereas the WikiHop [3]} dataset is about multi-hop reasoning across multiple documents. WikiHop is created using Wikipedia articles and Wikidata tuples. The head entity of a tuple and the relation together form a question. The tail entity of the tuple is the answer to that question. They map these tuples to Wikipedia articles in such a way that the head entity and tail entity of a tuple do not appear in a single document. Thus, models need to consider more that one document to answer the question. OpenBookQA [4]} is another RC dataset where questions come from elementary science facts. To answer these questions, models need to focus on multiple scientific facts and commonsense knowledge.
